Catheter Localization Utilizing a Sensor-Enabled Guidewire: Design of a Proof-of-Concept System

The purpose of this thesis project was to develop a proof-of-concept system for tracking the tip of a catheter without an embedded electromagnetic sensor by utilizing a sensor enabled guidewire. The motivation for the project was a reduction fluoroscopy radiation dose for clinicians in the interventional cardiology lab and the extension of navigation technology to be used with a wider variety of interventional devices through the implementation of expanded capabilities of the Abbott MediGuide system. The focus of the project was on the development of a proof-of-concept system capable of using an external device to track relative guidewire and catheter motion and apply that to a calculated position in the vasculature. The research conducted covered multiple disciplines from mechanical design to software algorithms. A prototype system was developed that functions alongside the MediGuide system to provide a three dimensional depiction of catheter location and a measurement of the relative linear displacement separating the distal tip of the guidewire and the distal tip of the catheter. The system consists of an electromechanical device to measure relative motion and software to communicate with the device, interpret recorded guidewire position data into a representative trajectory, and display the results to the user. The hardware and software components of the project were evaluated to determine accuracy and precision. The prototype device was determined to be accurate to 0.7±0.03% of total displacement. In a simulated use procedure the device was determined to be accurate to 1.4±0.53mm. The software algorithms to generate a simulated guidewire path were evaluated and tuned to generate the best response to the data sets available. In summary, the work performed here shows the possibility of implementing a device and software system that can provide localization information to the operator about the catheters used in an interventional procedure without the need for a sensor in the catheter

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD